type-checking Index objects • Wrote fast time series merging / joining methods in Cython. Will be integrated later into DataFrame.join and related functions 1.6. v.0.4.3 through v0.4.1 (September 25 - October Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing • An integrated group by engine for aggregating and transforming data sets • Date range generation (DateRange) more than 1 data structure? The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container

type-checking Index objects • Wrote fast time series merging / joining methods in Cython. Will be integrated later into DataFrame.join and related functions 1.7. v.0.4.3 through v0.4.1 (September 25 - October Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing • An integrated group by engine for aggregating and transforming data sets • Date range generation (DateRange) more than 1 data structure? The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container

type-checking Index objects • Wrote fast time series merging / joining methods in Cython. Will be integrated later into DataFrame.join and related functions 1.8. v.0.4.3 through v0.4.1 (September 25 - October Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing • An integrated group by engine for aggregating and transforming data sets • Date range generation (DateRange) more than 1 data structure? The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container

Python data analysis toolkit, Release 0.15.2 • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex considers the dtype when choosing an ‘empty’ value (GH7941). Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [92]: s = s Out[94]: 0 NaN 1 2 2 3 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). In [95]:

pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex considers the dtype when choosing an ‘empty’ value (GH7941). Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [92]: s = toolkit, Release 0.15.1 1 2 2 3 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). In [95]:

type-checking Index objects • Wrote fast time series merging / joining methods in Cython. Will be integrated later into DataFrame.join and related functions 64 Chapter 1. What’s New CHAPTER TWO INSTALLATION Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing • An integrated group by engine for aggregating and transforming data sets • Date range generation (date_range) more than 1 data structure? The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container

pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex considers the dtype when choosing an ‘empty’ value (GH7941). Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [87]: s = s Out[89]: 0 NaN 1 2 2 3 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). 84 Chapter

pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex considers the dtype when choosing an ‘empty’ value (GH7941). Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [78]: s = Out[80]: 0 NaN 1 2.0 2 3.0 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). In [81]:

pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex considers the dtype when choosing an ‘empty’ value (GH7941). Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [78]: s = Out[80]: 0 NaN 1 2.0 2 3.0 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). In [81]:

pandas, please upgrade to NumPy >= 1.7.0 (GH7711) • Highlights include: – The Categorical type was integrated as a first-class pandas type, see here – New scalar type Timedelta, and a new index type TimedeltaIndex powerful Python data analysis toolkit, Release 0.20.3 Previously, assigning to None in numeric containers changed the dtype to object (or errored, depending on the call). It now uses NaN: In [78]: s = Out[80]: 0 NaN 1 2.0 2 3.0 dtype: float64 NaT is now used similarly for datetime containers. For object containers, we now preserve None values (previously these were converted to NaN values). In [81]: